Apply the model¶
libraries¶
In [ ]:
import matplotlib.pyplot as plt
from math import pi
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn import preprocessing, model_selection
from sklearn import decomposition
from sklearn.model_selection import train_test_split
from sklearn import metrics
from matplotlib import rcParams
from matplotlib import pyplot
import plotly.express as px
rcParams['axes.titlepad'] = 10
In [ ]:
df =pd.read_csv("heart_clean.csv")
df.head()
Out[Â ]:
| age | gender | cholesterol | blood pressure | heart rate | smoking | alcohol intake | exercise hours | family history | diabetes | obesity | stress level | blood sugar | exercise induced angina | chest pain type | heart disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 75 | Female | 228 | 119 | 66 | 2 | 2 | 1 | 0 | 0 | 1 | 8 | 119 | 1 | Atypical Angina | 1 |
| 1 | 48 | Male | 204 | 165 | 62 | 2 | 0 | 5 | 0 | 0 | 0 | 9 | 70 | 1 | Typical Angina | 0 |
| 2 | 53 | Male | 234 | 91 | 67 | 0 | 2 | 3 | 1 | 0 | 1 | 5 | 196 | 1 | Atypical Angina | 1 |
| 3 | 69 | Female | 192 | 90 | 72 | 2 | 0 | 4 | 0 | 1 | 0 | 7 | 107 | 1 | Non-anginal Pain | 0 |
| 4 | 62 | Female | 172 | 163 | 93 | 0 | 0 | 6 | 0 | 1 | 0 | 2 | 183 | 1 | Asymptomatic | 0 |
In [ ]:
columns_to_convert_obj = [
'smoking',
'alcohol intake',
'family history',
'diabetes',
'obesity',
'exercise induced angina',
'heart disease'
]
df[columns_to_convert_obj] = df[columns_to_convert_obj].astype(object)
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 age 1000 non-null int64 1 gender 1000 non-null object 2 cholesterol 1000 non-null int64 3 blood pressure 1000 non-null int64 4 heart rate 1000 non-null int64 5 smoking 1000 non-null object 6 alcohol intake 1000 non-null object 7 exercise hours 1000 non-null int64 8 family history 1000 non-null object 9 diabetes 1000 non-null object 10 obesity 1000 non-null object 11 stress level 1000 non-null int64 12 blood sugar 1000 non-null int64 13 exercise induced angina 1000 non-null object 14 chest pain type 1000 non-null object 15 heart disease 1000 non-null object dtypes: int64(7), object(9) memory usage: 125.1+ KB
In [ ]:
from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV
from xgboost import XGBClassifier
X=df.drop('heart disease', axis=1)
y=df['heart disease']
X=pd.get_dummies(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)
# hyperparameters
params = {
'n_estimators': [100, 200, 300, 400, 500],
'learning_rate': [0.001, 0.01, 0.1, 0.2],
'max_depth': [3, 5, 7, 9, 12],
'min_child_weight': [1, 3, 5, 7, 10],
'subsample': [0.6, 0.7, 0.8, 0.9, 1.0],
'colsample_bytree': [0.6, 0.7, 0.8, 0.9, 1.0],
'reg_alpha': [0.0, 0.1, 0.5, 1.0],
'reg_lambda': [0.0, 0.1, 0.5, 1.0],
'gamma': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5],
'scale_pos_weight': [1, 2, 3, 5, 10]
}
# initialise
model = XGBClassifier(verbosity=0, objective='binary:logistic')
# search best hyperparameters with GridSearchCV
grid_search = GridSearchCV(estimator=model, param_grid=params, cv=5, scoring='recall')
grid_search.fit(X_train, y_train)
print("Best Hyperparameters:", grid_search.best_params_)
print("Best Score:", grid_search.best_score_)
# Visualise results
results = grid_search.cv_results_
plt.figure(figsize=(12, 6))
plt.plot(results['mean_test_score'], label='Mean Test Score')
plt.plot(results['mean_train_score'], label='Mean Train Score')
plt.legend()
plt.xlabel('Parameter Sets')
plt.ylabel('Score')
plt.title('Grid Search Results')
plt.show()